Many efforts have been spent to improve the ability to extract meaning data out of images captured by video and still cameras. Such abilities are being used in several applications, such as consumer, industrial, medical, and business applications. Many cameras are deployed in the streets, airports, schools, banks, offices, residencies—as standard security measures. These cameras are used either for allowing an operator to remotely view security events in real time, or for recording and analyzing a security event at some later time.
The introduction of new technologies is shifting the video surveillance industry into new directions that significantly enhance the functionality of such systems. Several processing algorithms are used both for real-time and offline applications. These algorithms are implemented on a range of platforms from pure software to pure hardware, depending on the application. However, these platforms are usually designed to simultaneously process a relatively small number of incoming image sequences, due to the substantial computational resources required for image processing. In addition, most of the common image processing systems are designed to process only uncompressed image data, such as the system disclosed in U.S. Pat. No. 6,188,381. Modern networked video environments require efficient processing capability of a large number of compressed video steams, collect from a plurality of image sources.
Increasing operational demands, as well as cost constraints created the need for automation of event detection. Such event detection solutions provide a higher detection level, save manpower, replace other types of sensors and lower false alarm rates.
Although conventional solutions am available for automatic intruder detection, license plate identification, facial recognition, traffic violations detection and other image based applications, they usually support few simultaneous video sources, using expensive hardware platforms that require field installation, which implies high installation, maintenance and upgrade costs.
Conventional surveillance systems employ digital video networking technology and automatic event detection. Digital video networking is implemented by the development of Digital Video Compression technology and the availability of IP based networks. Compression standards, such as MPEG-4 and similar formats allow transmitting high quality images with a relatively narrow bandwidth.
A major limiting factor when using digital video networking is bandwidth requirements. Because it is too expensive to transmit all the cameras all the time, networks are designed to concurrently transmit data, only from few cameras. The transmission of data only from cameras that are capturing important events at any given moment is crucial for establishing an efficient and cost effective digital video network.
Automatic video-based event detection technology becomes effective for this purpose. This technology consists of a series of algorithms that are able to analyze the camera image in real time and provide notification of a special event, if it occurs. Currently available event-detection solutions use conventional image processing methods, which require heavy processing resources. Furthermore, they allocate a fixed processing power (usually one processor) per each camera input. Therefore, such systems either provide poor performance due to resources limitation or are extremely expensive.
As a result, the needs of large-scale digital surveillance installations—namely, reliable detection, effective bandwidth usage, flexible event definition, large-scale design and cost, cannot be met by any of the current automatic event detection solutions.
Video Motion Detection (VMD) methods are disclosed, for example, in U.S. Pat. No. 6,349,114, WO 02/37429, in U.S. Patent Application Publication 2002,041,626, in U.S. Patent Application Publication No. 2002,054,210, in WO 01/63937, in EP1107609, in EP1173020, in U.S. Pat. No. 6,384,862, in U.S. Pat. No. 6,188,381, in U.S. Pat. No. 6,130,707, and in U.S. Pat. No. 6,069,655. However, all the methods described above have not yet provided satisfactory solutions to the problem of effectively obtaining meanings knowledge, in real time, from a plurality of concurrent image sequences.
It is an object of the present invention to provide a method and system for obtaining meaningful knowledge, from a plurality of concurrent image sequences, in real time.
It is another object of the present invention to provide a method and system for obtaining meaningful knowledge, from a plurality of concurrent image sequences, which are cost effective.
It is a further object of the present invention to provide a method and system for obtaining meaningful knowledge, from a plurality of concurrent image sequences, with reduced amount of bandwidth resources.
It is still another object of the present invention to provide a method and system for obtaining meaningful knowledge, from a plurality of concurrent image sequences, which is reliable, and having high sensitivity in noisy environments.
It is yet another object of the present invention to provide a method and system for obtaining meaningful knowledge, from a plurality of concurrent image sequences, with reduced installation and maintenance costs.
Other objects and advantages of the invention will become apparent as the description proceeds.